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Handwritten Digit Recognition Using Machine Learning Algorithms
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Handwritten Digit Recognition using Machine Learning Algorithms
By S M Shamim, Mohammad Badrul Alam Miah, Angona Sarker, Masud Rana
& Abdullah Al Jobair
Abstract- Handwritten character recognition is one of the practically important issues in pattern
recognition applications. The applications of digit recognition includes in postal mail sorting,
bank check processing, form data entry, etc. The heart of the problem lies within the ability to
develop an efficient algorithm that can recognize hand written digits and which is submitted by
users by the way of a scanner, tablet, and other digital devices. This paper presents an approach
to off-line handwritten digit recognition based on different machine learning technique. The main
objective of this paper is to ensure effective and reliable approaches for recognition of
handwritten digits. Several machines learning algorithm namely, Multilayer Perceptron, Support
Vector Machine, Naïve Bayes, Bayes Net, Random Forest, J48 and Random Tree has been used
for the recognition of digits using WEKA.
Keywords: pattern recognition, handwritten recognition, digit recognition, machine learning, WEKA,
off-line handwritten recognition, machine learning algorithm, neural network, classification
algorithm.
GJCST-D Classification: I.7.5, I.2.7, I.5.m
HandwrittenDigitRecognitionusingMachineLearningAlgorithms
Strictly as per the compliance and regulations of:
Global Journal of Computer Science and Technology: D
Neural & Artificial Intelligence
Volume 18 Issue 1 Version 1.0 Year 2018
Type: Double Blind Peer Reviewed International Research Journal
Publisher: Global Journals
Online ISSN: 0975-4172 & Print ISSN: 0975-4350

Handwritten Digit Recognition using Machine
Learning Algorithms
S M Shamim α
, Mohammad Badrul Alam Miah σ
, Angona Sarker ρ
, Masud Rana Ѡ
& Abdullah Al Jobair ¥
Abstract- Handwritten character recognition is one of the
practically important issues in pattern recognition applications.
The applications of digit recognition includes in postal mail
sorting, bank check processing, form data entry, etc. The heart
of the problem lies within the ability to develop an efficient
algorithm that can recognize hand written digits and which is
submitted by users by the way of a scanner, tablet, and other
digital devices. This paper presents an approach to off-line
handwritten digit recognition based on different machine
learning technique. The main objective of this paper is to
ensure effective and reliable approaches for recognition of
handwritten digits. Several machines learning algorithm
namely, Multilayer Perceptron, Support Vector Machine, Naïve
Bayes, Bayes Net, Random Forest, J48 and Random Tree has
been used for the recognition of digits using WEKA. The result
of this paper shows that highest 90.37% accuracy has been
obtained for Multilayer Perceptron.
Keywords: pattern recognition, handwritten recognition,
digit recognition, machine learning, WEKA, off-line
handwritten recognition, machine learning algorithm,
neural network, classification algorithm.
I. Introduction
ntelligent image analysis is an appealing research
area in Artificial Intelligence and also crucial for a
variety of present open research difficulties.
Handwritten digits recognition is a well-researched
subarea within the field that is concerned with learning
models to distinguish pre-segmented handwritten digits.
It is one of the most important issues in data mining,
machine learning, pattern recognition along with many
other disciplines of artificial intelligence [1].The main
application of machine learning methods over the last
decade has determined efficacious in conforming
decisive systems which are competing to human
performance and which accomplish far improved than
manually written classical artificial intelligence systems
used in the beginnings of optical character recognition
technology [2]. However, not all features of those
specific models have been previously inspected.
A great attempt of research worker in machine
learning and data mining has been contrived to achieve
efficient approaches for approximation of recognition
from data [3]. In twenty first Century handwritten digit
communication has its own standard and most of the
times in daily life are being used as means of
conversation and recording the information to be shared
with individuals. One of the challenges in handwritten
characters recognition wholly lies in the variation and
distortion of handwritten character set because distinct
community may use diverse style of handwriting, and
control to draw the similar pattern of the characters of
their recognized script.
Identification of digit from where best
discriminating features can be extracted is one of the
major tasks in the area of digit recognition system. To
locate such regions different kind of region sampling
techniques are used in pattern recognition [4].The
challenge in handwritten character recognition is mainly
caused by the large variation of individual writing styles
[5]. Hence, robust feature extraction is very important to
improve the performance of a handwritten character
recognition system. Nowadays handwritten digit
recognition has obtained lot of concentration in the area
of pattern recognition system sowing to its application in
diverse fields. In next days, character recognition
system might serve as a cornerstone to initiate
paperless surroundings by digitizing and processing
existing paper documents.
Handwritten digit dataset are vague in nature
because there may not always be sharp and perfectly
straight lines. The main goal in digit recognition is
feature extraction is to remove the redundancy from the
data and gain a more effective embodiment of the word
image through a set of numerical attributes. It deals with
extracting most of the essential information from image
raw data [6]. In addition the curves are not necessarily
smooth like the printed characters. Furthermore,
characters dataset can be drawn in different sizes and
the orientation which are always supposed to be written
on a guideline in an upright or downright point.
Accordingly, an efficient handwritten recognition system
can be developed by considering these limitations. It is
quiet exhausting that sometimes to identify hand written
characters as it can be seen that most of the human
beings can’t even recognize their own written scripts.
Hence, there exists constraint for a writer to write
apparently for recognition of handwritten documents.
Before revealing the method used in conducting
this research, software engineering module is first
presented. Pattern recognition along with Image
processing plays compelling role in the area of
handwritten character recognition. The study [7],
describes numerous types of classification of feature
extraction techniques like structural feature based
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© 2018 Global Journals
Author α σ ρ Ѡ ¥: Department of Information and Communication
Technology, Mawlana Bhashani Science and Technology University,
Santosh, Tangail-1902. e-mail: ictshamim@yahoo.com
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methods, statistical feature based methods and global
transformation techniques. Statistical approaches are
established on planning of how data are selected. It
utilizes the information of the statistical distribution of
pixels in the image. The paper [8], provided SVM based
offline handwritten digit recognition system. Authors
claim that SVM outperforms in the experiment.
Experiment is carried out on NIST SD19 standard
dataset. The study [9] provide the conversion of
handwritten data into electronic data, nature of
handwritten characters and the neural network approach
to form machine competent of recognizing hand written
characters. The study [10] addresses a comprehensive
criterion of handwritten digit recognition with various
state of the art approaches, feature representations, and
datasets. However, the relationship of training set size
versus accuracy/error and the dataset-independence of
the trained models are analyzed. The paper [11]
presents convolution neural networks into the
handwritten digit recognition research and describes a
system which can still be considered state of the art.
II. Methods and Materials
a) Multilayer Perceptions
A neural network based classifier, called Multi-
Layer perception (MLP), is used to classify the
handwritten digits. Multilayer perceptron consists of
three different layers, input layer, hidden layer and
output layer. Each of the layers can have certain number
of nodes also called neurons and each node in a layer is
connected to all other nodes to the next layer [12]. For
this reason it is also known as feed forward network. The
number of nodes in the input layer depends upon the
number of attributes present in the dataset. The number
of nodes in the output layer relies on the number of
apparent classes exist in the dataset. The convenient
number of hidden layers or the convenient number of
nodes in a hidden layer for a specific problem is hard to
determine. But in general, these numbers are selected
experimentally. In multilayer perceptron, the connection
between two nodes consists of a weight. During training
process, it basically learns the accurate weight
adjustment which is corresponds to each connection
[13]. For the learning purpose, it uses a supervised
learning technique named as Back propagation
algorithm.
b) Support Vector Machine
SVM or Support Vector Machine is a specific
type of supervised ML method that intents to classify the
data points by maximizing the margin among classes in
a high-dimensional space [14]. SVM is a representation
of examples as points in space, mapped due to the
examples of the separate classes are divided by a fair
gap that is as extensive as possible. After that new
examples are mapped into that same space and
anticipated to reside to a category based on which side
of the gap they fall on [15]. The optimum algorithm is
developed through a “training” phase in which training
data are adopted to develop an algorithm capable to
discriminate between groups earlier defined by the
operator (e.g. patients vs. controls), and the “testing”
phase in which the algorithm is adopted to blind-predict
the group to which a new perception belongs [16]. It
also provides a very accurate classification performance
over the training records and produces enough search
space for the accurate classification of future data
parameters. Hence it always ensures a series of
parameter combinations no less than on a sensible
subset of the data. In SVM it’s better to scale the data
always; because it will extremely improve the results.
Therefore be cautious with big dataset, as it may leads
to the increase in the training time.
c) J48
The J48 algorithm is developed for the MONK
project along with WEKA [17]. The algorithm is an
extension for C4.5 decision tree algorithm [18]. There
are many options for tree pruning in case of J48
algorithm. The classification algorithms convenient in
WEKA try to clarify the results or prune. This method will
help us to produces more generic results and also can
be used to correct potential over fitting issues. J48 helps
to recursively classify until each of the leaf is pruned,
that is to classify as close knit to the data. Hence this will
helps to ensure the accuracy, although excessive rules
will be produced. However pruning will cause to less
accuracy of a model on training data. This is due to
pruning employs various means to relax the specificity
of the decision tree, hopefully improving its performance
on the test data. The complete concept is to
increasingly generalize a decision tree until it gains a
balance of accuracy together with flexibility. The J48
applies two pruning methods. First one is known as
subtree replacement. This concludes that nodes in the
decision tree can be replaced with a leaf -- which
reduces the number of tests along a particular path. This
process begins from the leaves of the completely
formed tree, and attempts backwards toward the root.
Second category of pruning adopted in J48 is termed
subtree rising. In this respect, a node can be moved
upwards towards the root of the tree, replacing other
nodes another way. Subtree rising repeatedly has a
insignificant effect on decision tree models. There is
generally no clear way to anticipate the utility of the
option, though it may be desirable to try turning it off if
the induction process is catching a long time. This is
because of the fact that subtree rising may be
somewhat computationally complicated. Error rates are
needed to make actual conclusions about which parts
of the tree to rise or replace. There exist multiple ways to
perform this. The straight forward way is to reserve a
portion of the training data in order to test on decision
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tree. Reserved portion may then be adopted as test
data for the decision tree, aiding to reduce potential over
fitting. This method is recognized as reduced-error
pruning. Though the approach is straightforward, it also
decreases the overall volume of data available for
training the model. For specifically small datasets, it may
be advisable to avert using reduced error pruning.
d) Random Forest Algorithm
Random forest as is an ensemble of un-pruned
regression or classification trees, activated from
bootstrap samples of the training data, adopting
random feature selection in the tree imitation process.
The prediction is made by accumulating the predictions
of the ensemble by superiority voting for classification. It
returns generalization error rate and is more potent to
noise. Still, similar to most classifiers, RF may also suffer
from the curse of learning from an intensely imbalanced
training data set. Since it is constructed to mitigate the
overall error rate, it will tend to focus more on the
prediction efficiency of the majority class, which
repeatedly results in poor accuracy for the minority
class.
e) Naive Bayes
The Naive Bayes classifier [19] contributes a
simple method, representing and learning probabilistic
knowledge with clear semantics. It is termed naive due
to it relies on two important simplifying assumes that
predictive attributes are conditionally self-reliant given
the class, and it considers that no hidden attributes
influence the prediction method. It is a probabilistic
classifier which relies upon Bayes theorem with robust
and naive independence assumptions. It is one of the
best basic text classification approaches with numerous
applications in personal email sorting, email spam
detection, sexually explicit content detection, document
categorization, sentiment detection, language detection
[20]. Although the naïve design and oversimplified
assumptions that this approach uses, Naive Bayes
accomplishes well in many complicated real-world
problems. All though it is often out performed by other
approaches such as boosted trees, Max Entropy,
Support Vector Machines, random forests etc, Naive
Bayes classifier is very potent as it is less
computationally intensive (in both memory and CPU)
and it needs a small extent of training data. Moreover,
the training time with Naive Bayes is considerably
smaller as opposed to alternative approaches.
f) Bayes Net
Bayesian networks are a powerful probabilistic
representation, and their use for classification has
received considerable attention [21]. It reflects the
states of some part of a world that is being modeled
and it describes how those states are related by
probabilities. Bayesian network is a graphical model that
encodes probabilistic relationships among variables of
interest. When used in conjunction with statistical
techniques, the graphical model has several advantages
for data analysis. One, because the model encodes
dependencies among all variables, it readily handles
situations where some data entries are missing. Two, a
Bayesian network can be used to learn causal
relationships, and hence can be used to gain
understanding about a problem domain and to predict
the consequences of intervention. This classifier learns
from training data the conditional probability of each
attribute given the class label [22, 23].
g) Random Tree
The algorithm may deal with both regression
and classification problems. Random trees is a
ensemble of tree predictors which is called forest .The
classification performs as follows: random trees
classifier takes the input feature vector, categories it with
individual tree in the forest, outputs the class label which
received the most of “votes”. In the event of a
regression, the classifier response is the average of the
responses over all the trees in the forest [24]. In random
tree algorithm all the trees are trained with the same
parameters but on different training sets. These sets are
created from the original training set adopting the
bootstrap procedure and for each training set, randomly
choose the same number of vectors as in the initial set.
The vectors are chosen with replacement. That is, some
vectors will occur more than once and some will be
absent. In random trees there is no need for any
accuracy estimation techniques, like cross-validation or
bootstrap, or a separate test set to obtain an estimate of
the training error. The error is estimated internally during
the training.
h) Dataset Description
The handwritten digit recognition is a extensive
research topic which gives a comprehensive survey of
the area including major feature sets, learning datasets,
and algorithms [25]. Contrary to optical character
recognition which focuses on recognition of machine-
printed output, where special fonts can be used and the
variability between characters along with the same size,
font, and font attributes is fairly small.
The feature extraction and the classification
technique play an important role in offline character
recognition system performance. Various feature
extraction approaches have been proposed for
character recognition system [26]. The problems faced
in handwritten numeral recognition has been studied
while using the techniques like Dynamic programming,
HMM, neural network, Knowledge system and
combinations of above techniques [27]. Wider ranging
work has been carried out for digit recognition in so
many languages like English, Chinese, Japanese, and
Arabic. In Indian mainly worked in Devanagari, Tamil,
Telugu and Bengali numeral recognition [28].
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In our experiment we used digit dataset
provided by Austrian Research Institute for Artificial
Intelligence, Austria. This data set indicate that arbitrary
scaling and a blur setting of 2.5 for the Mitchell down-
sampling filter should perform well and used down-
sample to 16x16 pixels.
Figure 1: A small portion of handwritten dataset example
This dataset is divided in two parts training set
and testing set. Training set has 1893 samples and test
set has 1796 samples. The detail of the dataset is
provided in [29].
III. Experimental Tools
Waikato Environment for Knowledge Analysis
(WEKA) is a prominent suite of machine learning which
is written in Java and developed at the University of
Waikato. It is free software accessible under the GNU
General Public License. It contains a collection of
algorithms and visualization tools for predictive
modelling, data analysis, along with graphical user
interfaces for smooth access to this functionality [30]. It
supports various standard data mining tasks, more
particularly, data pre-processing, classification,
visualization, clustering, feature selection, regression.
All of Weka's approaches are predicated on the
assumption that the data is convenient as a single flat
file or relation, where each data point is characterized
through a fixed number of attributes [31].
WEKA has numerous user interfaces. Its main
user interface is the Explorer, however essentially the
same functionality can be accessed by the component-
based Knowledge Flow interface and from the
command line. The Experimenter allows the systematic
comparison of the predictive performance of the Weka's
machine learning algorithms on an accumulation of
datasets.
IV. Experimental Result and Discussion
WEKA has several graphical user interfaces that
enable easy access to the underlying functionality. To
gauge and investigate the performance on the selected
methods or algorithms namely Support Vector Machine,
Multilayer Perceptron, Random Forest Algorithm,
Random Tree, Naïve Bayes, Bayes Net and j48 Decision
tree algorithms are used. We use the same experiment
procedure as suggested by WEKA.
In WEKA, all dataset is considered as instances
and features in the data are also known as attributes.
The experiment results are partitioned into several sub
division for easier analysis and evaluation. In the first
part, correctly and incorrectly classified instances will be
divided in numeric and percentage value and
subsequently Kappa statistic, mean absolute error and
root mean squared error will be in numeric value.
Experiment shows the relative absolute error and root
relative squared error in percentage (%) for references
and in evaluation process. Our simulation results are
shown in below tables-1 and tables-2. In table-1 mainly
summarizes the result based on accuracy and time
taken for each simulation in our experiment. Moreover,
table-2 shows the result based on error during the
simulation in WEKA.
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Table 1: Simulation result based on accuracy and time consumption
Table 2: Simulation result based on different error
Based on the above table-1, the highest
accuracy is 90.37 % and the lowest is 75.06%. The other
algorithm yields an average accuracy of around 83.89%.
In fact, the highest accuracy belongs to the Multilayer
Perceptron classifier, followed by Support Vector
Machine with a percentage of 87.97% and subsequently
Random Forest Algorithm 85.75%, Bayes Net 84.35%,
Naïve Bayes 81.85%, j48 79.51% and Random Tree
75.06%. Kappa statistics value ranges from 0 to 1. Value
0 means totally disagreement and 1 means full
agreement. It checks the reliability of Classifying
algorithm on dataset. The total time, mean absolute
error, root mean absolute error, relative absolute error
and root relative absolute error is also a crucial
parameter to build the model in comparing the different
classification algorithm. Mean absolute error is the mean
of overall error made by classification algorithm and
least the error will be best classifier. In the table-2
Multilayer Perceptron has least 0.023 mean absolute
errors among all seven algorithms.
In [32] experimental results reveal that it is
possible to train a face detector without having to label
images as containing a face or not. Their experiment is
only sensitive to high-level concepts such as cat faces
and human bodies. Multi-column deep neural networks
for image classification have been presented in [33].
They only improve the state-of-the-art on a plethora of
common image classification benchmarks. Supervised
learning unsupervised learning, reinforcement learning &
evolutionary computation, and indirect search for short
programs encoding deep and large networks has been
presented in [34]. They only proposed how different
Name of Algorithms
Correctly
Classified
Instances
% (value)
Incorrectly
Classified
Instances
% (Value)
Time
Taken
(seconds)
Kappa
Statistic
Multilayer Perceptron 90.37 9.63 3.15 0.893
Support Vector
Machine
87.97 12.03 0.56 0.8664
Random Forest 85.75 14.25 0.44 0.8416
Bayes Net 84.35 15.65 0.86 0.8262
Naïve Bayes 81.85 18.15 3.45 0.7983
J48 79.51 20.49 0.53 0.7722
Random Tree 85.6 24.94 0.55 0.7228
Name of Algorithms
Mean
Absolute
Error
Root Mean
Squared Error
Relative
Absolute
Error (%)
Root Relative
Squared Error (%)
Multilayer Perceptron 0.023 0.1231 12.78 41.04
Support Vector
Machine
0.1611 0.2734 89.49 91.15
Random Forest 0.0593 0.1532 32.97 51.06
Bayes Net 0.0312 0.1745 17.36 58.15
Naïve Bayes 0.0361 0.1879 20.06 62.65
J48 0.0444 0.1957 24.66 65.25
Random Tree 0.0499 0.2234 27.72 74.45
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technique can be used for pattern recognition. In [35]
recognition of handwritten bangla basic characters and
digits using convex hull based feature set has been
proposed. Their experiment result shows that with a
database of 10000 samples, the maximum recognition
rate of 76.86% is observed for handwritten Bangla
characters. Online and offline handwritten Chinese
character recognition has been proposed in [36]. Their
experiment result reported that the highest test
accuracies 89.55% for offline. In our experiment different
machine learning algorithm has been used for handwrite
digit recognition and obtained highest 90.37% accuracy
obtained for Multilayer Perceptron.
V. Conclusion
The main objective of this investigation is to find
a representation of isolated handwritten digits that allow
their effective recognition. In this paper used different
machine learning algorithm for recognition of
handwritten numerals. In any recognition process, the
important problem is to address the feature extraction
and correct classification approaches. The proposed
algorithm tries to address both the factors and well in
terms of accuracy and time complexity. The overall
highest accuracy 90.37% is achieved in the recognition
process by Multilayer Perceptron. This work is carried
out as an initial attempt, and the aim of the paper is to
facilitate for recognition of handwritten numeral without
using any standard classification techniques.
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Global
Journal
of
Computer
Science
and
Technology
Volume
XVIII
Issue
I
Version
I
23
Year
2018
(
)
D
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